Productivity change using growth accounting and frontier-based approaches: evidence from a Monte Carlo analysis

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Abstract

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivitygrowth estimates derived from growthaccounting (GA) and frontier-based methods (namely data envelopment analysis-, corrected ordinary least squares-, and stochastic frontier analysis-based malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.
Original languageEnglish
Pages (from-to)673-683
Number of pages11
JournalEuropean Journal of Operational Research
Volume222
Issue number3
DOIs
Publication statusPublished - 1 Nov 2012

Keywords

  • data envelopment analysis
  • productivity and competitiveness
  • Monte Carlo analysis
  • stochastic frontier analysis
  • growth accounting

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